Abstract
In this paper, a novel cooperative charging strategy for electric vehicles tuned by a constraint programming algorithm has been proposed. The implemented model handles heterogeneous and large-scale residential areas by not only reducing the EV peak charging load, but also improving the user satisfaction levels. The evaluation of the capability of the proposed model at both individual and aggregated levels is considered through various scenarios. The simulated results prove the potential of the proposed cooperative EV strategy in outperforming the uncoordinated EV charging model in terms of peak-to-average ratio reduction, user satisfaction level, and load factor improvement in addition to the suppression of the peak load increase. Furthermore, comparative analysis with existing models shows that the proposed algorithm can manage more complex policies and is performed significantly and efficiently.
Highlights
T HE major challenge of renewable energy integration is to control the intermittent power generation as much as possible under the hazard of continuously and short-term changing weather conditions
Direct load control (DLC) in the residential sector is the most widely discussed Demand response (DR) method for scheduling aggregated power of a group of participants [6], in which the utility remotely switches off electrical loads based on user preferences, such as electric water heaters (EWH), heating, ventilation and air-conditioning (HVAC) unit, clothes dryer system (CD), and electric vehicle(EV)
constraint programming (CP) optimization-based technique was used for reaching the optimum electric vehicles (EVs) load profile with two main parameters to conduct the charging schedules, viz., emergency charging index, and EV charging priority level
Summary
T HE major challenge of renewable energy integration is to control the intermittent power generation as much as possible under the hazard of continuously and short-term changing weather conditions (minutes or seconds). The impact of EV charging on the infrastructure of the electric power system has been evaluated in multiple studies, and can be commonly outlined in two main effects: improving the shape of the energy demand profile (e.g. minimizing the peak load, and PAR) [10], or using the EV as a distributed energy storage appliance that could supply the electricity demand to user buildings or to the grid during on-peak periods to minimize energy bills and losses [11], [12]. The control of EV charging strategies is divided into three main groups: clustering, forecasting, and scheduling [13] Their common objective is to reduce the impact of high EV charging penetration at the distribution level.
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